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Update of D S +   + 

Update of D S +   + . Liming Zhang & Sheldon Stone (Syracuse University). Outline. Results based on 314 pb -1 (data38-41) + 288 pb -1 (data47-48)=602 pb -1. D s - ( D s * - ) D s * + ( D s + ) TAGGED SIDE: (9 modes).

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Update of D S +   + 

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  1. Update of DS+ + Liming Zhang & Sheldon Stone (Syracuse University)

  2. Outline • Results based on 314 pb-1 (data38-41) + 288 pb-1 (data47-48)=602 pb-1

  3. Ds-(Ds*-) Ds*+ (Ds+) TAGGED SIDE: (9 modes) SIGNAL SIDE: K+K-p- KsK- p- '(p+p-)p- h'(rg) p- K+K-p-p0 instead offr p+p-p- K*-K*0 (KsK-p+p-) r-h + or t+(p+n)n + g Find  and look at MM2 Look at MM*2 Analysis Techniques e+e- (4170 MeV)

  4. Tag side mbc in [2.015, 2.067] GeV to select DS* DS events 12 MeV mass window for Ks  150 MeV mass window for r+ and r0  100 MeV mass window for K* 3 s pullmass for p and h  10 MeV mass window for h'  hpp (new from previous analysis)  20 MeV mass window and helicity angle of r decay |cosa|<0.8 for h'  gr [it’s sin2adistribution] P(p/p0)>100 MeV in KKp, KKpp0, ppp modes to remove D*+ ~2010 MeV MDs window: 17.5 MeV MM*2 window: [3.782, 4.00] GeV2 Shower from Ds* Not hot No track match Pass E9/E25 Energy > 30 MeV if goodBarrel or >50 MeV if goodEndcap Selection Criteria of Tags

  5. Determination of # of Tags

  6. 2D Fit to Obtain # of tags • 2D Binned Extended ML fit by RooFit • Fitting region: |MDs-mDs|<50 MeV, MM*2[3.5, 4.25] GeV2 • Three components of PDFs • Signal • BG1: real Ds combining with fake g • BG2: fake Ds • PDFs: production of PDFs in MDs and MM*2 • Signal: 2G(MDs) x CB(MM*2) • BG1: 2G(MDs) x P5(MM*2) • BG2: P1(MDs) x P5(MM*2) • Total 20(shape)+3(yields)-2(fixed for CB)=21 free parameters • Generic ddmix MC Data41 used (~9 times current dataset) to test this method

  7. Double Tag Study (Data) All 9 modes combined a and n obtained and to be fixed in ST fitting MM*2 (GeV2)

  8. MM*2 Distributions in mass 17.5 MeV KKp hp KS0K BG1 BG2 h'p KKpp0 ppp hr K*-K*0 h'(rg)p

  9. Systematic Error on # of tags • Default fit is P5 for BG1 and BG2, all are allowed to float • Systematic error estimation • BG2: increase/reduce 1 order for MM*2 polynomial • BG1: fix its shape to MC, where its normalization is floated • We record the difference with the default fit, and imply to each of modes • We add all differences in quadrature as the systematic error

  10. Data Fit Results (All DATA) # in MDs17.5 MeV and MM*2 [3.782, 4.00] GeV2 Total stat. error = 936 (2.1%) Total syst. error = 894 (2.0%) Differences of modes added linearly to get the total systematic error

  11. Independent method to check # of tags

  12. Mbc Distribution • If DS tag comes from prompt DS , it peaks at mbc • If DS tag comes from prompt DS*, it’s flat at mbc • [2.015, 2.067] GeV window used to select Tags • In a wider window [2.0, 2.08], the amounts of DS (peaking curve) and DS*(flat curve) are the same, and the efficiency ratio is 1 (test by MC 1.007  0.009) • i.e. N(DS) = N(DS*) • Now we concentrate on DS* DS 2.067 2.015 DS*

  13. C1 A1 B A2 C2 Idea • We look at DmS = m(Dsg) - m(Ds) • Tag from Ds* (flat) peaks in DmS, tag from Ds (peaking in mbc) is flat • Divide in three regions [Using A & B because A is clean, B have large BG • N(Ds*) = A + B + C • Total number of tags we used is N(Ds) + N(Ds*) – C = 2 N(Ds*) – C = 2(A+B) + C A=A1+A2 C=C1+C2 A, B, C are the # in the flat curve

  14. DS* signal DS signal Fake gamma BG Fake DS BG DmS Distributions from MC DS mass and MM*2 signal windows applied to get DmS distribution • Fitted by CB function, a and n obtained from double tag • Sideband subtraction to remove this background

  15. Double Tagging Signal DmS distribution (DS subtracted), to obtain a and n DS subtraction: Events in A - 0.09  B; 0.09 is ratio of # in A to B for DS signal (peak in mbc) MC DATA Single Tag in red Events / 2 MeV DmS (GeV) DmS (GeV)

  16. DATA Fit • Fit to mass sideband subtracted distribution of all modes • Two backgrounds: DS signal (in green) and fake g (in blue) • Their shapes are fixed to MC, normalizations allowed to float A = 17125  434 B = 3513  230 C = 2077  136 Ntag = 2(A+B) + C = 43353  991 1% difference Ntag = 43859  936 from the MM*2 fit

  17. Signal side of m+n or t+(p+n)n

  18. Signal Dsm v Reconstruction • Only one extra track with opposite sign of charge to the tag [using “TrkmanApproved”] • The track has cos (opening angle) <0.90 [previous 0.81] • Eff. increase 11% • Resolution not change comparing with 0.81 • No any neutral energy cluster detected (Emax) > 300 MeV • Kaon veto using RICH [if ng(k)>2, and L(m)-L(K)>10] • Calculate MM2 and apply kinematical constrains fit with two hypotheses that g from signal side or tag side. The fits gives two fitted MM2, we choose the one with the lower fit c2 • Best photon candidate selection: if there is more than one photon candidates with the same Ds in a event we choose only the lowest c2 choice

  19. 160 54 63 Data MM2 Distribution We use [-0.1,0.2] GeV2 as the fit region to eliminatehigher sideK0p+(~0.25 GeV2) and hp+ (~0.3 GeV2) background Comparison of Candidates Lum. Ratio is 1.42 Case 1 [-0.05, 0.05] GeV2 5% lower

  20. Signal m+n MC Efficiency Eff’s and shapes are weighted according to # of tags in the data

  21. Tag bias from MC It’s easy to find tags in Dsmunu events than in the typical Ds decays

  22. Ds+ t+n; t+  p+n MC Eff = 40.9% in [-0.1,0.2] GeV2

  23. Backgrounds from Ds We study the other background sources as considered in the paper Expected # for data in [-0.1,0.2] GeV2 Case 2 Case 1 From MC

  24. Fit Technique • Fit detail: a 2D fit • All Shapes in MM2 except fake Ds are fixed to MC, but normalizations are allowed to float • The signal shape in MDs (double Gaussian) obtained from Data when determining Ntag, and fixed • Fake Ds is described by a second order in MM2 and a first order polynomial in MDs, and the three shape parameters are allowed to float Generic MC fit (sum of two cases) Ntag = 336771+-1934 Nmunu = 1838.7+-36.9Br(Ds->munu)=(0.607+-0.013)% Input 0.61%

  25. Data Fit (Sum two cases) •  &  dependent fit • Constrain N/N = Rbr  Reff =1.059  0.455 = 0.482 • Rbr=R  B(t+p+n); R = 9.72 B = (10.900.07)% • Breff (Ds  ) = (0.603  0.037)% # in 17.5 MeV MDs & [-0.1, 0.2] MM2 region

  26. Data Fit (case 1) •  &  independent fit • Br (Ds  ) = (0.568  0.045)% # in 17.5 MeV MDs & [-0.1, 0.2] MM2 region

  27. Simultaneous Fit to case 1&2 •  &  independent fit • Fix  area in case 1 / 2 to 98.8%/1.2% • Fix  area in case 1 / 2 to 55%/45% • Br (Ds  t) = (6.7  0.8)% Plots need to be provide

  28. Cross check: DsK0K+ Signal MC Resolution: s1*f+(1-f)*s2  (GeV2) Munu MC     0.0346+-0.0002 K0K MC      0.0344+-0.0003 K0K Data 0.0353+-0.0015 Data Fit • Yield 1066  42 • Eff: (77.0  0.6)% • Br(DsK0K+) = (3.15  0.14)% • Consistent with Peter’s • Br(DsKSK+) = (1.49  0.07 0.05)%

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